An image alignment technology is a foundational technology in the field of image processing. With rapid development of digital imaging, a series of applications based on the image alignment technology have emerged. These applications include generation of a panorama, generation of a highly dynamic image, information fusion of two images, and the like. For example, a color image is restored using an infrared image, or a blur of another color image is removed using an image with noise.
With vigorous development of various image capture devices, how to align multi-modal multi-spectral images becomes a new problem, and these multi-modal multi-spectral images include a near infrared image, a color image, a depth image, a nuclear magnetic resonance image, an ultrasonic image, and the like. Because of different capture devices and a dynamic nature of a capture scenario, there is a great difference in captured images. FIG. 1 shows four groups of common multi-modal multi-spectral images, which are as follows from left to right. The first group includes images with different exposures, the second group includes color and depth images, the third group includes color and near infrared images, and the fourth group includes an image shot when a flash is enabled and an image shot when a flash is disabled. It can be learned from FIG. 1 that main differences between the multi-modal multi-spectral images are as follows, a large color contrast between the images, and large gradient value and gradient direction contrasts between the images.
A conventional alignment technology based on a scale-invariant feature transform (SIFT) feature point has been widely used in the image alignment field. Further, in the technology, images that need to be aligned are matched by searching for the SIFT feature point. Two images are used as an example. According to an image alignment method based on the SIFT feature point, SIFT feature point vectors of the two images are first extracted, and a nearest neighbor is found using a Euclidean distance between the vectors in order to obtain a correspondence between the two images. However, the SIFT feature point is closely related to a gradient value and a gradient direction that are between images, the image alignment technology based on the SIFT feature point greatly depends on gradient value and gradient direction consistencies that are between regions with similar image structures. However, it can be learned from FIG. 1 that, for multi-modal multi-spectral images, there is a relatively large gradient direction contrast between the regions with similar structures. Therefore, the image alignment technology based on the SIFT feature point is not suitable for alignment between the multi-modal multi-spectral images.